Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-24T03:23:45.409Z Has data issue: false hasContentIssue false

Autonomous multirobot excavation for lunar applications

Published online by Cambridge University Press:  25 January 2017

Jekanthan Thangavelautham*
Affiliation:
School of Earth and Space Exploration, Arizona State University, 781 E Terrace Mall, Tempe, AZ 85287, USA
Kenneth Law
Affiliation:
David Schaeffer and Associates, Markham, ON, Canada. E-mail: [email protected]
Terence Fu
Affiliation:
University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada. E-mails: [email protected], [email protected], [email protected]
Nader Abu El Samid
Affiliation:
MDA Space Missions, 9445 Airport Road, Brampton, ON L6S 4J3, Canada. E-mail: [email protected]
Alexander D. S. Smith
Affiliation:
University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada. E-mails: [email protected], [email protected], [email protected]
Gabriele M. T. D'Eleuterio
Affiliation:
University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada. E-mails: [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper, a control approach called Artificial Neural Tissue (ANT) is applied to multirobot excavation for lunar base preparation tasks including clearing landing pads and burying of habitat modules. We show for the first time, a team of autonomous robots excavating a terrain to match a given three-dimensional (3D) blueprint. Constructing mounds around landing pads will provide physical shielding from debris during launch/landing. Burying a human habitat modules under 0.5 m of lunar regolith is expected to provide both radiation shielding and maintain temperatures of −25 °C. This minimizes base life-support complexity and reduces launch mass. ANT is compelling for a lunar mission because it does not require a team of astronauts for excavation and it requires minimal supervision. The robot teams are shown to autonomously interpret blueprints, excavate and prepare sites for a lunar base. Because little pre-programmed knowledge is provided, the controllers discover creative techniques. ANT evolves techniques such as slot-dozing that would otherwise require excavation experts. This is critical in making an excavation mission feasible when it is prohibitively expensive to send astronauts. The controllers evolve elaborate negotiation behaviors to work in close quarters. These and other techniques such as concurrent evolution of the controller and team size are shown to tackle problem of antagonism, when too many robots interfere reducing the overall efficiency or worse, resulting in gridlock. Although many challenges remain with this technology, our work shows a compelling pathway for field testing this approach.

Type
Articles
Copyright
Copyright © Cambridge University Press 2017 

References

1. Abu El Samid, N., Lunar Excavation Methods and Evaluation Master's Thesis (University of Toronto, Institute for Aerospace Studies, Toronto, Canada, 2008).Google Scholar
2. Abu El Samid, N., Thangavelautham, J. and D'Eleuterio, G., “Infrastructure Robotics: A Technology Enabler for Lunar in-situ Resource Utilization, Habitat Construction and Maintenance,” Proceedings of International Astronautic Conference (2008).Google Scholar
3. Albus, J., “A theory of cerebellar function,” Math. Biosci, 10, 2561 (1971).Google Scholar
4. Balovnev, V., New Methods for Calculating Resistance to Cutting of Soil (Amerind Publishing (Translation), New York, New York, 1983)Google Scholar
5. Barfoot, T. D. and D'Eleuterio, G. M. T., “An Evolutionary Approach to Multiagent Heap Formation,” Proceedings of the 1999 Congress on Evolutionary Computation (CEC), (1999), pp. 427–435.Google Scholar
6. Beckers, R., Holland, O. E. and Deneubourg, J. L., “From Local Actions to Global Tasks: Stigmergy and Collective Robots,” Proceedings of the 4th International Workshop on the Syntheses and Simulation of Living Systems (MIT Press, 1994) pp. 181189.Google Scholar
7. Bernard, D., Doraist, G., Gamble, E., Kanefskyt, B., Kurien, J., Man, G. K., Millart, W., Muscettolao, N., Nayak, U., Rajant, K., Rouquette, N., Smith, B., Taylor, W. and wen Tung, Y., “Spacecraft Autonomy Flight Experience: The ds1 Remote Agent Experiment,” Proceedings of the AIAA, (1999) pp. 28–30.Google Scholar
8. Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, New York, 1999).Google Scholar
9. Bonabeau, E., Theraulaz, G., Deneubourg, J.-L., Aron, S. and Camazine, S., “Self-organization in Social Insects,” Trends Ecol. Evol., 12, 188193 (1997).CrossRefGoogle ScholarPubMed
10. Bradley, D. and Seward, D., “The development, control and operation of an autonomous robotic excavator,” J. Intell. Robot. Syst. 21, 7397 (1998).Google Scholar
11. Bristow, K. and Holt, J., “Can termites create local energy sinks to regulate mound temperature?J. Therm. Biol. 12, 1921 (1997).Google Scholar
12. Cannon, S. and Singh, S., “Models for Automated Earthmoving,” Proceedings from the International Symposium on Experimental Robotics (1999).Google Scholar
13. Cao, Y., Fukunaga, A. and Kahng, A., “Cooperative mobile robotics: Antecedents and directions,” Auton. Robots 4, 123 (1997).Google Scholar
14. Chantemargue, F., Dagaeff, T., Schumacher, M., and Hirsbrunner, B.. Coopération implicite et performance. In Proceedings of the Sixth symposium on Cognitive Sciences (ARC), Villeneuve d'Ascq, France, December 10–12 (1996).Google Scholar
15. Crabbe, F. L. and Dyer, M. G., “Second-Order Networks for Wall-Building Aagents,” Proceedings of the International Joint Conference on Neural Networks, vol. 3 (1999) pp. 2178–2183.Google Scholar
16. Dunbabin, M. and Corke, P., “Autonomous excavation using a rope shovel,” J. Field Robot. 23 (6–7), 379394 (2006).Google Scholar
17. Garthwaite, J., Charles, S. L. and Chess-Williams, R., “Endothelium-derived relaxing factor release on activation of nmda receptors suggests role as intercellular messenger in the brain,” Nature 336 (6197), 385388 (1988).CrossRefGoogle ScholarPubMed
18. Goldberg, D., Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, NY, 1986).Google Scholar
19. Grassé, P., “La Reconstruction du nid les Coordinations Interindividuelles; la Theorie de Stigmergie,” Insectes Sociaux, 35, 4184 (1959).Google Scholar
20. Halbach, E., Zhmud, V. and Halme, A., “Simulation of Robotic Regolith Mining for Base Construction on Mars,” Proceedings of the 12th Symposium on Advanced Space Technologies in Automation and Robotics (ASTRA) (2013) pp. 1–7.Google Scholar
21. Heiken, G., Vaniman, D. and French, B., Lunar Sourcebook: A User's Guide to the Moon (Cambridge University Press, Cambridge, 1991).Google Scholar
22. Hinton, G., “Shape Representation in Parallel Systems,” Proceedings of the 7th International Joint Conference on Artificial Intelligence (1981) pp. 1088–1096.Google Scholar
23. Husbands, P., “Evolving Robot Behaviours with Diffusing Gas Networks,” Proceedings of EvoRobots (1998) pp. 71–86.Google Scholar
24. Jacobs, R., Jordan, M. and Barto, A., “Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks,” Cogn. Sci. 15 (2), 219250 (1991).Google Scholar
25. Khademian, B. and Hashtrudi-Zaad, K., “Shared control architectures for haptic training: Performance and coupled stability analysis,” The International Journal of Robotics Research, 30 (13), 16271642 (2011).Google Scholar
26. Lee, D. and Spong, M., “Semi-Autonomous Teleoperation of Multiple Cooperative Robots for Human-Robot Lunar Exploration,” Proceedings of the AAAI Spring Symposium (2006) pp. 1–8.Google Scholar
27. Lu, Z., Huang, P., Liu, Z. and Meng, Z., “Stability conditions for asymmetric dual-user shared control method with uncertain time delay,” IEEE, 2015 IEEE International Conference on Information and Automation, pp. 1997–2002 (2015).Google Scholar
28. Matarić, M. J., Nilsson, M. and Simsarian, K. T., “Cooperative Multi-Robot Box-Pushing,” Proceedings of the IEEE/RSJ IROS (1995) pp. 556–561.Google Scholar
29. Miller, D. P. and Machulis, K., “Visual Aids for Lunar Rover Tele-Operation,” Proceedings of 8th International Symposium on Artificial Intelligence: Robotics and Automation in Space (2005).Google Scholar
30. Montague, P. R., Gally, J. A. and Edelman, G. M., “Spatial signaling in the development and function of neural connections,” Cerebral Cortex 1 (3), 199220 (1991).Google Scholar
31. Napp, N. and Nagpal, R., “Distributed Amorphous Ramp Construction in Unstructured Environments,” Proceedings of the Distributed Autonomous Robotic Systems (DARS '12) (2012) pp. 1–15.Google Scholar
32. Parker, C. A., Zhang, H. and Kube, R. C., “Blind Bulldozing: Multiple Robot Nest Construction,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2003) pp. 2010–2015.Google Scholar
33. Rowe, P. and Stentz, A., “Parameterized Scripts for Motion Planning,” Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems (1997).Google Scholar
34. Skonieczny, K. and Wettergreen, D., “Advantages of continuous excavation in lightweight planetary robotic operations,” 35, 11211139 (2016).Google Scholar
35. Stanley, K. and Miikkulainen, R., “Continual Coevolution Through Complexification,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002) (Kaufmann, 2002) pp. 113–120.Google Scholar
36. Stentz, A., Bares, J., Singh, S. and Rowe, P., “A Robotic Excavator for Autonomous Truck Loading,” Proceedings of IEEE/RSJ International Conference on Intelligent Robotic Systems, vol. 1 (1998) pp. 175–186.Google Scholar
37. Stewart, R. and Russell, A., “Emergent Structures Built by a Minimalist Autonomous Robot using a Swarm-Inspired Template Mechanism,” Proceedings of the 1st Australian Conference on ALife (ACAL 2003) (2003) pp. 216–230.Google Scholar
38. Stewart, R. and Russell, A., “Building a Loose Wall Structure with a Robotic Swarm Using a Spatio-Temporal Varying Template,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2004) pp. 712–716.Google Scholar
39. Thangavelautham, J., A Regulatory Theory of Cortical Organization and Its Applications to Robotics PhD Thesis (University of Toronto, Toronto, Canada, 2008).Google Scholar
40. Thangavelautham, J., Barfoot, T. and D'Eleuterio, G. M. T., “Coevolving Communication and Cooperation for Lattice Formation Tasks (Updated),” Advances in Artificial Life: Proceedings of the 7th European Conference on ALife (ECAL) (2003) pp. 857–864.Google Scholar
41. Thangavelautham, J. and D'Eleuterio, G., “Tackling learning intractability through topological organization and regulation of cortical networks,” IEEE Trans. Neural Netw. Learn. Syst. 23, 552564 (2012).Google Scholar
42. Thangavelautham, J. and D'Eleuterio, G. M. T., “A Coarse-Coding Framework for a Gene-Regulatory-Based Artificial Neural Tissue,” Advances in Artificial Life: Proceedings of the 8th European Conference on ALife (2005) pp. 67–77.Google Scholar
43. Thangavelautham, J., El Samid, N., Grouchy, P., Earon, E., Fu, T., Nagrani, N. and D'Eleuterio, G., “Evolving Multirobot Excavation Controllers and Choice of Platforms Using an Artificial Neural Tissue Paradigm,” Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) (2009) pp. 258–265.Google Scholar
44. Thangavelautham, J., Smith, A., Abu El Samid, N., Ho, A., Boucher, D., Richard, J. and D'Eleuterio, G., “Multirobot Lunar Excavation and ISRU Using Artificial-Neural-Tissue Controllers,” Proceedings of the Space Technology and Applications International Forum (2008).CrossRefGoogle Scholar
45. Thangavelautham, J., and D'Eleuterio, G. M. T., “A Neuroevolutionary Approach to Emergent Task Decomposition,” Proceedings of the 8th Parallel Problem Solving from Nature Conference, vol. 1 (2004) pp. 991–1000.Google Scholar
46. Trianni, V. and Dorigo, M., “Self-Organisation and Communication in Groups of Simulated and Physical Robots,” Biological Cybernetics, vol. 95 (Springer, Berlin, Heidelberg, 2006) pp. 213231.Google Scholar
47. Wawerla, J., Sukhatme, G. and Matari, M., “Collective Construction with Multiple Robots,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2002) pp. 2696–2701.Google Scholar
48. Wilson, M., Melhuish, C., Sendova-Franks, A. B. and Scholes, S., “Algorithms for Building Annular Structures with Minimalist Robots Inspired by Brood Sorting in Ant Colonies,” Autonomous Robots, vol. 17 (2004) pp. 115–136.Google Scholar
49. Xidias, E., Zacharia, P. and Nearchou, A., “Path planning and scheduling for a fleet of autonomous vehicles,” Robotica, 34, 22572273 (2016).Google Scholar